scikit-learn
diff --git a/‎examples/model_selection/plot_precision_recall.py
Lines changed: 25 additions & 9 deletions b/‎examples/model_selection/plot_precision_recall.py
Lines changed: 25 additions & 9 deletions
@@ -139,20 +139,36 @@
 plt.legend(loc="lower left")
 plt.show()
 
-# Plot Precision-Recall curve for each class
+# Plot Precision-Recall curve for each class and iso-f1 curves
 plt.clf()
-plt.plot(recall["micro"], precision["micro"], color='gold', lw=lw,
-         label='micro-average Precision-recall curve (area = {0:0.2f})'
-               ''.format(average_precision["micro"]))
+f_scores = np.linspace(0.2, 0.8, num=4)
+lines = []
+labels = []
+for f_score in f_scores:
+    x = np.linspace(0.01, 1)
+    y = f_score * x / (2 * x - f_score)
+    l, = plt.plot(x[y >= 0], y[y >= 0], color='gray', alpha=0.2)
+    plt.annotate('f1={0:0.1f}'.format(f_score), xy=(0.9, y[45] + 0.02))
+
+lines.append(l)
+labels.append('iso-f1 curves')
+l, = plt.plot(recall["micro"], precision["micro"], color='gold', lw=lw)
+lines.append(l)
+labels.append('micro-average Precision-recall curve (area = {0:0.2f})'
+              ''.format(average_precision["micro"]))
 for i, color in zip(range(n_classes), colors):
-    plt.plot(recall[i], precision[i], color=color, lw=lw,
-             label='Precision-recall curve of class {0} (area = {1:0.2f})'
-                   ''.format(i, average_precision[i]))
-
+    l, = plt.plot(recall[i], precision[i], color=color, lw=lw)
+    lines.append(l)
+    labels.append('Precision-recall curve of class {0} (area = {1:0.2f})'
+                  ''.format(i, average_precision[i]))
+
+fig = plt.gcf()
+fig.set_size_inches(7, 7)
+fig.subplots_adjust(bottom=0.25)
 plt.xlim([0.0, 1.0])
 plt.ylim([0.0, 1.05])
 plt.xlabel('Recall')
 plt.ylabel('Precision')
 plt.title('Extension of Precision-Recall curve to multi-class')
-plt.legend(loc="lower right")
+plt.figlegend(lines, labels, loc='lower center')
 plt.show()